Prediction of treatment response and outcome of transarterial chemoembolization in patients with hepatocellular carcinoma using artificial intelligence: A systematic review of efficacy

PURPOSE: To perform a systematic literature review of the efficacy of different AI models to predict HCC treatment response to transarterial chemoembolization (TACE), including overall survival (OS) and time to progression (TTP). METHODS: This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines until May 2, 2024. RESULTS: The systematic review included 23 studies with 4,486 HCC patients. The AI algorithm receiver operator characteristic (ROC) area under the curve (AUC) for predicting HCC response to TACE based on mRECIST criteria ranged from 0.55 to 0.97. Radiomics-models outperformed non-radiomics models (AUCs: 0.79, 95 %CI: 0.75-0.82 vs. 0.73, 0.61-0.77, respectively). The best ML methods used for the prediction of TACE response for HCC patients were CNN, GB, SVM, and RF with AUCs of 0.88 (0.79-0.97), 0.82 (0.71-0.89), 0.8 (0.60-0.87) and 0.8 (0.55-0.96), respectively. Of all predictive feature models, those combining clinic-radiologic features (ALBI grade, BCLC stage, AFP level, tumor diameter, distribution, and peritumoral arterial enhancement) had higher AUCs compared with models based on clinical characteristics alone (0.79, 0.73-0.89; p = 0.04 for CT + clinical, 0.81, 0.75-0.88; p = 0.017 for MRI + clinical versus 0.6, 0.55-0.75 in clinical characteristics alone). CONCLUSION: Integrating clinic-radiologic features enhances AI models' predictive performance for HCC patient response to TACE, with CNN, GB, SVM, and RF methods outperforming others. Key predictive clinic-radiologic features include ALBI grade, BCLC stage, AFP level, tumor diameter, distribution, and peritumoral arterial enhancement. Multi-institutional studies are needed to improve AI model accuracy, address heterogeneity, and resolve validation issues.